Semantic Segmentation

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21 papers in the last four weeks, up 24% on the four weeks before. 0.2% of all new papers.

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Latest papers 255

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Modeling The Object Representations Underlying Human Physical Reasoning
Feb 12, 2026Andrey Gizdov, Andrea Procopio, Lorenzo Caputi +4Physical Reasoning Computational Cognitive Modeling
Harvard University · Weizmann Institute of Science · Bocconi University
GaussianCaR: Gaussian Splatting for Efficient Camera-Radar Fusion
Feb 9, 2026Santiago Montiel-Marín, Miguel Antunes-García, Fabio Sánchez-García +3Camera-Radar Fusion Autonomous Driving Perception
Department of Electronics. University of Alcalá, Spain. · Department of Cognitive Robotics. Delft University of Technology, The Netherlands.
Multi-encoder ConvNeXt Network with Smooth Attentional Feature Fusion for Multispectral Semantic Segmentation
Feb 8, 2026Leo Thomas Ramos, Angel D. SappaSemantic Segmentation Remote Sensing Image Segmentation
Computer Vision Center, Universitat Autònoma de Barcelona, Barcelona, 08193, Spain · ESPOL Polytechnic University, Guayaquil, 090112, Ecuador
Learning to Segment Liquids in Real-world Images
Jan 2, 2026Jonas Li, Michelle Li, Luke Liu +2Image Segmentation Boundary-Aware Segmentation
Department of Computer Science and Engineering, University of North Texas, Denton, TX 76203, USA
DWFF-Net: A Multi-Scale Farmland System Habitat Identification Method with Adaptive Dynamic Weight Feature Fusion
Nov 11, 2025Kesong Zheng, Zhi Song, Peizhou Li +4Agricultural Image Analysis Multi-Scale Feature Fusion
National Engineering Research Center of Geographic Information System, China University of Geosciences, Wuhan, China · College of Science, Shenyang Agricultural University, Liaoning, China · College of Engineering, Shenyang Agricultural University, Liaoning, China +2
AD-SAM: Adapting the Segment Anything Model for Semantic Segmentation in Autonomous Driving
Oct 30, 2025Het Patel, Mario Camarena, Fatemeh Nazari +3Vision Foundation Model Adaptation Autonomous Driving Perception
Department of Computer Science, The University of Texas Rio Grande Valley, Edinburg, TX 78539 USA · Department of Civil Engineering, The University of Texas Rio Grande Valley, Edinburg, TX 78539 USA · Department of Computer Science and Engineering, University of California Riverside, Riverside, CA 92521 USA
AURASeg: Attention-Guided Upsampling with Residual-Assisted Boundary Refinement for Drivable-Area Segmentation
Oct 24, 2025Narendhiran VijayakumarAutonomous Driving Perception Boundary-Aware Segmentation
Improved Robustness from Biologically Inspired Sparse Contrast Representations
Sep 29, 2025Lorena Stracke, Lia Nimmermann, Shashank Agnihotri +3Distribution Shift Robustness Image Segmentation
Media Systems, University of Siegen, Germany · Data and Web Science Group, University of Mannheim, Germany · Analogue Circuits and Image Sensors, University of Siegen, Germany +1
JVLGS: Joint Vision-Language Gas Leak Segmentation
Aug 27, 2025Xinlong Zhao, Qixiang Pang, Shan DuSemantic Segmentation
University of British Columbia - Okanagan · University of Central Missouri
Through the Looking Glass: A Dual Perspective on Weakly-Supervised Few-Shot Segmentation
Aug 22, 2025Jiaqi Ma, Guo-Sen Xie, Fang Zhao +1Weakly Supervised Image Segmentation Weakly Supervised Semantic Segmentation
School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China · School of Intelligence Science and Technology, Nanjing University, Suzhou 215163, China
SynSeg: Feature Synergy for Multi-Category Contrastive Learning in End-to-End Open-Vocabulary Semantic Segmentation
Aug 8, 2025Weichen Zhang, Kebin Liu, Fan Dang +3Contrastive Learning Weakly Supervised Image Segmentation
Tsinghua University, 100084, Beijing, China · Beijing Jiaotong University, 100044, Beijing, China
From Pixels to Images: A Structural Survey of Deep Learning Paradigms in Remote Sensing Image Semantic Segmentation
May 21, 2025Quanwei Liu, Tao Huang, Jiaqi Yang +1Remote Sensing Image Segmentation Semantic Segmentation
College of Science and Engineering, James Cook University, Cairns QLD 4878, Australia · Center for AI and Data Science Innovation, James Cook University, Cairns QLD 4878, Australia · Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, Madison, WI 53706 USA +1
SARTM: Segment Any RGB Thermal Model with Language aided Distillation
May 4, 2025Dong Xing, Jinhe Zhang, Hang Yang +1Vision Foundation Model Adaptation Semantic Segmentation
Changchun Institute of Optics, Fine Mechanicsand Physics, Chinese Academy of Sciences University
Asynchronous Federated Continual Segmentation with Evolving Clients and Label Spaces
Mar 19, 2025Can Peng, Qianhui Men, Pramit Saha +5Federated Learning Aggregation Federated Continual Learning
University of Oxford, Oxford, United Kingdom · University of Bristol, Bristol, United Kingdom
SegCol Challenge: Semantic Segmentation for Tools and Fold Edges in Colonoscopy data
Dec 20, 2024Xinwei Ju, Rema Daher, Razvan Caramalau +7Endoscopy Image Segmentation
UCL Hawkes Institute, University College London, London, UK · Department of Computer Science, University College London, London, UK · Medtronic, London, UK +3

Feb 12, 2026, cs.CVJ/K move · Enter open · S save

Modeling The Object Representations Underlying Human Physical Reasoning

Andrey Gizdov, Andrea Procopio, Lorenzo Caputi, Georgi I. Ivanov+3

Harvard University · Weizmann Institute of Science · Bocconi University

Physical Reasoning Computational Cognitive Modeling Object-Centric Representation Learning Representation Alignment Semantic Segmentation

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Humans appear to represent objects when reasoning about physics with coarse, volumetric "bodies" that smooth concavities, trading fine visual detail for efficient physical predictions. Yet, the structure of these representations remains largely unknown. Segmentation models, in contrast, are trained for pixel-accurate masks that may misalign with such bodies. We ask whether and when these models nonetheless acquire human-like object representations. Using a time-to-collision (TTC) and change detection (CD) behavioral task with data from 178 and 50 human participants, respectively, we introduce a pipeline and an alignment metric to compare the visual representations of segmentation models to those of humans. We do this systematically on multiple architectures (DINOv2, SegFormer, DeepLabV3+, and UPerNet), varying their size and training time. We find that briefly trained models segment objects too coarsely, aligning poorly with humans, while fully trained models segment objects too finely. For each model, there is an intermediate training regime that best matches the coarse bodies observed in human behaviour, and larger models tend to reach it earlier. We show these bodies emerge under resource constraints in general-purpose vision models, providing computational support to resource-rational accounts of human cognition. This work provides a foundational framework for testing alignment between vision models and humans and shows there is a growing gap between the state-of-the-art in artificial intelligence and human cognition, driven by scaling model size and training.

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